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Big Genome DataSheds Light on Cancer Causes
1
Steven G Rozen steverozen [at] gmail.comhttps://www.duke-nus.edu.sg/content/rozen-steve
https://admissions.duke-nus.edu.sg/adm/Director, Duke-NUS Centre for Computational Biology
https://www.duke-nus.edu.sg/research/centres/centre-computational-biologyProfessor, Program in Cancer and Stem Cell Biology
IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
(IEEE CIBCB 2016) http://www.cibcb2016.org/October 5-7, 2016,
Chiang Mai, Thailand
and Can Improve Prevention and Treatment
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Singapore• Small: 50 km (31 mi) from
east to west and 26 km (16 mi) north-sourth
• 5.7 million people
• Rich: per capita GDP > US
• Good public health
• Life expectancy at birth 84.7 (US is 79.7)
• Infant mortality: 2.5/1,000 (US is 6/1,000)
http://kids.britannica.com/comptons/art-55105/Singapore
Duke-NUS Graduate Medical School Singapore
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• Nonprofit joint venture:– Duke in North Carolina, USA– National University of Singapore
(NUS)• On campus of 1,700 bed public
hospital and the National Cancer Center
• 60 MDs / year• 15 PhD students / year• PhD program in biostatistics and
bioinformatics: https://admissions.duke-nus.edu.sg/adm/
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Duke-NUS Centre for Computational Biology
Many areas of computational biology and bioinformatics
not just cancer genomicshttps://www.duke-nus.edu.sg/research/centres/centre-
computational-biology
Outline• Revolution in sequencing technology
– “Next generation sequencing” underlies tectonic shifts in approaches to biomedical research, especially in cancer
– Makes much heavier demands on computation, data storage, and bioinformatics
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Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer– Cancer genomics: a study of “global”
characteristics of tumors– Finding mutations that cause cancer is an
important use of next generation sequencing
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Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer• Finding signatures of processes that
cause mutations– Mutations cause cancer (partly)– Now we can find out what causes mutations
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Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer• Signatures of processes that cause
mutations • Applications and challenges for cancer
prevention and treatment– How genomics can improve cancer prevention and
treatment– How you can help
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Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer• Finding signatures of processes that
cause mutations • Applications and challenges for cancer
prevention and treatment
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Revolution in sequencing technology
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http:
//w
ww
.gen
ome.
gov/
sequ
enci
ngco
sts/
Cost
per
hum
an g
enom
e (U
S $)
Year
$10 millionIn 2007
10,000 Xdrop in price
$1.5 thousandtoday
Immense innovation in research techniques
Next generation sequencing(NGS) is a foundational technology
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Shendure & Aiden,Nat. Biotech. 2012
Next generation sequencing(NGS) will become a foundational technology
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Snippets of DNA or RNA are self-identifying (self-barcoded)
Then sequence the DNA or RNA segments
Use biochemical tricks to select for DNA or RNA segments physically associated with “biological process” of interest”
Applications of NGS
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Method To determine…DNA-Seq A genome sequence
Targeted DNA-Seq A subset of a genome (for example, an exome)
RNA-Seq RNA (that is, the transcriptome)Methyl-Seq Sites of DNA methylation, genome-wideTargeted methyl-Seq DNA methylation in a subset of the genome
DNase-Seq, Sono-Seq Active regulatory chromatin (nucleosome-depleted)
FAIRE-Seq (formaldehyde-assisted isolation of regulatory elements)
Active regulatory chromatin (nucleosome-depleted)
MAINE-Seq (MNase-assisted isolation of nucleosomes)
Histone-bound DNA (nucleosome positioning)
ChIP-Seq Protein-DNA interactions (using chromatin immunoprecipitation)
RIP-Seq (RNA-binding protein immunoprecipitation) Protein-RNA interactions
CLIP-Seq (cross-linking IP) Protein-RNA interactions
Adapted from Shendure and Aiden, Nat. Biotech. 2012
Applications of NGS
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Method To determine…DNA-Seq A genome sequence
Targeted DNA-Seq A subset of a genome (for example, an exome)
RNA-Seq RNA (that is, the transcriptome)Methyl-Seq Sites of DNA methylation, genome-wideTargeted methyl-Seq DNA methylation in a subset of the genome
DNase-Seq, Sono-Seq Active regulatory chromatin (nucleosome-depleted)
FAIRE-Seq (formaldehyde-assisted isolation of regulatory elements)
Active regulatory chromatin (nucleosome-depleted)
MAINE-Seq (MNase-assisted isolation of nucleosomes)
Histone-bound DNA (nucleosome positioning)
ChIP-Seq Protein-DNA interactions (using chromatin immunoprecipitation)
RIP-Seq (RNA-binding protein immunoprecipitation) Protein-RNA interactions
CLIP-Seq (cross-linking IP) Protein-RNA interactions
Adapted from Shendure and Aiden, Nat. Biotech. 2012
Many applications• Human variation / human genetics• Cancer genetics• Clinical applications in human
genetics and cancer genetics• Plant and animal breeding for
agriculture• Sequencing new genomes• Metagenomics• Pathogen discovery• Pathogen evolution
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Bioinformatics technologies for next-generation sequencing
• More data means need for more computing power
• Mixture of 1. On premises (Duke-NUS) high-performance computing (HPC)2. National Supercomputing Centre 3. Cloud computing
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High performance computingat Duke-NUS
• 0.5 petabytes• 720 cores
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1. On-premises computing HPC
• Advantages:Full range of analytical pipelines / softwareEasy software configuration, development and debuggingLess need for data transfers
• However processing and disk storage are limited
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2. National Supercomputing Centre• 10 petabytes• 30,000 cores• 128 nodes with
NVIDIA K40 GPUs for molecular dynamics, deep learning, etc.
• 100 Gps data network to US
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3. High performance cloud computing
20
DNAnexus cloud computing
21
DNAnexus cloud computing
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DNAnexus cloud computing
21h 59m $176.29
Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer• Finding signatures of processes that
cause mutations• Applications and challenges for cancer
prevention and treatment
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The broader vision for personalized cancer therapy• If we can understand each tumor in detail, we can
select the therapies that are most likely to be effective
• Will likely involve combinations of treatment because of tumors’ abilities to develop resistance
• I.e. a few cells in the tumor already have mutations that make them resistant one treatment, but no cells have mutations that make them resistant to two treatments
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Example: BRAF V600E mutationscause cancer, leading to treatment
25http://www.nzmu.co.nz/braf-inhibitors New Zealand
A 38-year-old man with BRAF-mutant melanoma and subcutaneous metastatic deposits Before treatment After 15 weeks After relapse (resistance)
at 23 weeks
Nikhil Wagle et al. JCO 2011;29:3085-3096
©2011 by American Society of Clinical Oncology
Our study of genomics ofbile duct cancer(cholangiocarcinoma, CCA)
27http://www.hopkinsmedicine.org/liver_tumor_center/conditions/bile_duct_cancer.html
28
Some CCA is caused by liver flukes (worms)
Sripa et al. 2007
“OV” (Opisthorcis viverrini)liver fluke
Koi pla(raw fresh-water fish tartar / salad)
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OV (liver fluke) infection and CCA
Sripa et al. 2007
Chiang Mai
BAP1 confirmed as a tumor suppressor innon-liver-fluke bile-duct cancer
Chan-On et al., Nat. Genet. 2013
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MLL3, a driver in liver-flukebile-duct cancer
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Ong et al., Nat. Genet. 2012
Liver-fluke versus non-fluke bile-duct cancers
Chan-On et al., Nat. Genet. 2013 32
Non-fluke Fluke
Implications for treatment?
• We are now working on new classifications based on more extensive studies partly based on differences between fluke and non-fluke CCAs
• Findings suggest potential avenues for treatments
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Fibroadenoma: benign but very common breast tumor
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Lim et al., Nat. Genet. 2014
Hot spot mutations in MED12 in 60% of tumors
Fibroadenoma: benign but very common breast tumor
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Lim et al., Nat. Genet. 2014
Hot spot mutations in MED12 in 60% of tumors
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Breast fibroepithelial tumors(fibroadenoma [FA] and phyllodes)
Tan et al, Nature Genet. 2015
Phyllodes
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Tan et al, Nature Genet. 2015
NR-LBD: ligand binding domainNR-DBD: DNAbinding domain
RARA (retinoic acid receptor alpha)
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Breast fibroepithelial tumors(fibroadenoma and phyllodes)
Tan et al, Nature Genet. 2015
Phyllodes
Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer• Finding signatures of mutagenic
processes• Applications and challenges for cancer
prevention and treatment
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*Per 100,000, age adjusted to the 2000 US standard population.Note: Due to changes in ICD coding, numerator information has changed over time. Rates for cancer of the liver, lung and bronchus, and colon and rectum are affectedby these coding changes.Source: US Mortality Volumes 1930 to 1959, US Mortality Data 1960 to 2008, National Center for Health Statistics, Centers for Disease Control and Prevention.
©2012, American Cancer Society, Inc., Surveillance Research
US
mal
e de
aths
/ 10
0,00
0, a
ge-a
djus
ted
Carcinogenic exposures are important
Lung and bronchus
Stomach
*Per 100,000, age adjusted to the 2000 US standard population.Note: Due to changes in ICD coding, numerator information has changed over time. Rates for cancer of the liver, lung and bronchus, and colon and rectum are affectedby these coding changes.Source: US Mortality Volumes 1930 to 1959, US Mortality Data 1960 to 2008, National Center for Health Statistics, Centers for Disease Control and Prevention.
©2012, American Cancer Society, Inc., Surveillance Research
US
mal
e de
aths
/ 10
0,00
0, a
ge-a
djus
ted
Carcinogenic exposures are important
Some carcinogenic exposure are mutagens, so recognizing the sources of mutagenic exposures by examining the mutations they cause can identify causes of cancer
Lung and bronchus
Stomach?
Mutation signature example:An herbal remedy
Aristolochia Plants
Herbal remediesAristolochic acid I
AA
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AdenineDNA adducts,
Adenine > Thymine mutations
Adapted from Poon et al, Science Translational Medicine, 2013
AA causes kidney failure
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AA causes extremely high numbersof somatic (i.e. tumor specific) mutations in upper tract urothelial carcinomas
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Who
le g
enom
eso
mati
c mut
ation
coun
ts
Poon et al, Science Translational Medicine, 2013
Location ofUTUCsAA
UTU
C
UV
mel
anom
a
Toba
cco
lung
Theoretically banned/restrictedbut multiple AA plant species readily available on web( 马兜铃 mǎ dōu líng)
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Acute kidney toxin and potent mutagen300g, $27.52 Free shipping
青木香 , qīng mù xiāng, contains AA
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天仙藤 , tiānxiān téng, contains AA
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广防己 , guǎng fáng jǐ, contains AA
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What does AA signature look like?
49Poon et al, Science Translational Medicine, 2013
A>T A>G A>CG>AG>CG>T
CAG>CTG
CAA>CTATAG>TTG
Transcribed versus non-transcribed
strand bias
Remember: we are focusing on what causes the mutations, not how the mutations cause cancer
Some tumors contain both AA mutationsand mutations due to other causes
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Poon et al, Genome Medicine, 2015
Overlays of mutations due to differentexogenous mutagens or endogenous mutagenic processes-- Computational separation
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Poon et al, Genome Medicine, 2015
AA
Overlays of mutations due to differentexogenous mutagens or endogenous mutagenic processes-- Computational separation
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Poon et al, Genome Medicine, 2015
AA
ABOBEC
AA
ABOBEC
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Poon et al, Genome Medicine, 2015
CpG > TpG (deamination of 5 methyl cytosine)
NMFNon-NegativeFactorization
Overlays of mutations due to differentexogenous mutagens or endogenous mutagenic processes-- Computational separation
Non negative matrix factorization (NMF)
Mutation signaturesMutations contributed by each signature
Mutations not present in the reconstructed catalog
Observed somatic mutation catalog of a tumor genome
W X
≈N signatures
K m
utati
on ty
pes
K m
utati
on ty
pes
N si
gnat
ures
T tumors
T tumors
Level of exposure of 1 tumor to 1 signature
H
V (observed mutations)
Adapted from Alexandrov 2013
One can estimate the contributioneach mutagen to the mutations in each tumor
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Poon et al, Genome Medicine, 2015
Important points on NMF• NMF is only a tool – best (lowest error) approximate
factorization does not necessarily correspond to any biological reality
• Models derived by NMF should be useful – provide information on exposures or mutational processes; "All models are wrong but some are useful"1
• Signature extraction and activity (exposure) assignment are separate; can have good signature extraction but poor activity assignment, because factorization is usually underdetermined
• Must combine NMF with additional information to find useful models
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1 George E. P. Box (1979), "Robustness in the strategy of scientific model building", in Launer, R. L.; Wilkinson, G. N., Robustness in Statistics, Academic Press, pp. 201–236.
Mutation signatures, state of the art
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CpG > TpG
Tobacco smoke
Activated APOBEC
Activated APOBEC
BRCA1/2 deficiency
. . . (14 more)
COSMIC http://cancer.sanger.ac.uk/cosmic/signatures, Ludmil Alexandrov, Serena Nik-Zainal, Mike Stratton
Mutation signatures, state of the art
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COSMIC http://cancer.sanger.ac.uk/cosmic/signatures, Ludmil Alexandrov, Serena Nik-Zainal, Mike Stratton
Mutation signatures, state of the art
• ~30 signatures in ~10,000 tumors (COSMIC web page http://cancer.sanger.ac.uk/cosmic/signatures)
• 6 due to known exogenous exposures• 14 have no known cause• Remainder due to endogenous factors (e.g.
polymerase epsilon mutation, DNA mismatch repair deficiency, other repair deficiency (e.g. BRCA1/2)
• Extended mutation signatures of many known carcinogens are unknown
59
Molecular epidemiology of AA
Aristolochia Plants
Herbal remediesAristolochic acid I
AA
60
Adenine DNA adducts,
Adenine > Thymine mutations
Adapted from Poon et al, Science Translational Medicine, 2013
A few years ago:AA exposure in upper tract urothelial cancer
Upper Tract Urothelial
Today: AA exposure in multiple tumor types
(whole genome)
Bladder
Upper Tract Urothelial
Kidney
Bile duct
Liver
Poon et al., 2013 and subsequent data (HCC)Zou et al., 2015 (CCA)Scelo et al., 2014 and Jelakovic et al., 2014 (RCC)Poon et al., 2013, Hoang et al., 2013 (UTUC)Poon et al., 2015 (Bladder)
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Taiwan (herbal medicine)
Grollman, 2013
Some areas of the Balkans (contamination of wheat flour with AA-containing seeds), in
association with Balkan endemic nephropathy
A few years ago:AA thought to be confined to a few geographical hot spots
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China, Taiwan, Singaporemultiple cancer types, population > 1 billion
Today: Molecular evidence (AA signature) for much more widespread exposure
AA in renal cell carcinomas from regions of Romania and Croatia
not associated with Balkan endemic nephropathy
Scelo et al., 2014 and Jelakovic et al., 2014
Non-molecular evidence thatAA exposure even more widespread
• India / South Asia, Aristolochia indica plants used in traditional medicine (population > 1 billion)
• South / Central America, Aristolochia plants used in traditional medicine; extent unclear
65
66
Evidence of widespread use of AA-containing plants in South Asia
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Evidence of widespread use of AA-containing plants in South Asia
Cultivated AA plant or AA plant
product purchased in market
AA inCentral American “snake bottle”
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Photograph by Donald HallUniversity of Florida
Aristolochia trilobata Battus polydamas
Photograph: Kimera Corporation
AA
Clinical implications ofWidespread AA exposure• Primary prevention (avoiding AA)
– Regulation, education– Possible: unlike tobacco, presumably non-addictive– Possible: unlike aflatoxin, ingested deliberately
• Secondary prevention: focused screening for people with known or likely exposure based on– Geography– Use of AA-containing remedies– Kidney failure– Previous AA-related cancer (e.g. based on signature)
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Next steps: causes of many mutation signatures are unknown
Unknown
Next steps: causes of many mutation signatures are unknown
Unknown
… and we do NOT know the signatures of 100s of known or suspected mutagenic carcinogens
Next steps: causes of many mutation signatures are unknown
Unknown
… and we do NOT know the signatures of 100s of known or suspected mutagenic carcinogens To resolve this: experimental studies
Next steps: causes of many mutation signatures are unknown
Unknown
… and we do NOT know the signatures of 100s of known or suspected mutagenic carcinogens To resolve this: experimental studies
Need experience and robust experimental systems
Outline• Revolution in sequencing technology• Cancer genomics and finding mutations
that cause cancer• Finding signatures of processes that
cause mutations • Applications and challenges for cancer
prevention and treatment
74
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Primary prevention
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Treatment
• Using genomic information (including mutations and gene expression) to select the best treatment
• Combination therapy seems to be the only plausible way around resistance
• Need a larger ‘n’ – how did patients with particular genomic characteristics respond to (combination therapy)
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How to get a larger ‘n’
• Will require sharing genomic and treatment outcome data across organizations
• Several efforts in this directions• One example…
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• International, multiphase, multiyear project
• Provide the “critical mass” of genomic and clinical data necessary to improve clinical decision making – i.e. large ‘n’
• Catalyze new clinical and translational research.
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GENIEUse Cases
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GENIEUse Cases
How you can help• Bioinformatics expertise is critical, and often
limiting, in much of basic and clinical research• The more you can take a broad perspective
and “own” the basic science or clinical questions, the more you will contribute
• More broadly your skills and advocacy needed to “turn the health-care system into a learning system”1
– Overcome technical, administrative, and political obstacles to data sharing for projects like GENIE
811 Goal articulated by Eric Lander
Acknowledgements (partial)Singapore / Duke NUS, National Cancer Centre, Singapore General Hospital and otherSong Ling PoonMi Ni HuangApinya JusakulJohn McPhersonWeng Khong LimPatrick TanBen Tean T e hChoon Kiat OngJing TanPuay Hoon TanBenita K T TanAye Aye Thike, Cedric Chuan Young NgWaraporn Chan-onMaaja-Liisa Nairismagi
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Taiwan / Chang Gung Memorial Hospital and otherSee-Tong PangWen-Hui WengCheng-Keng ChuangThailand / Khon Kaen University and otherVajarabhongsa BhudhisawasdiChutima SubimerbChawalit PairojkulSopit WongkhamPaiboon SithithaworPuangrat YongvanitFundingSingapore National Medical Research Council; A*STAR and the Singapore Ministry of Health through the Duke-NUS Signature Research Programs; Singapore Millennium Foundation; Lee Foundation, the National Cancer Centre Research Fund; The Verdant Foundation; Cancer Science Institute Singapore; Chang Gung Memorial Hospital; Taiwan National Science Council; Research Team Strengthening Grant, the National Genetic Engineering and Biotechnology Center and the National Science and Technology Development Agency, Thailand.
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